Static

class psynet.trial.static.StaticNetwork(*args, **kwargs)[source]

Bases: ChainNetwork

creation_time

the time at which the Network was created.

details

a generic column for storing structured JSON data

failed

boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason

an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

full

Whether the network is currently full

id

a unique number for every entry. 1, 2, 3 and so on…

max_size

How big the network can get, this number is used by the full() method to decide whether the network is full

property1

a generic column that can be used to store experiment-specific details in String form.

property2

a generic column that can be used to store experiment-specific details in String form.

property3

a generic column that can be used to store experiment-specific details in String form.

property4

a generic column that can be used to store experiment-specific details in String form.

property5

a generic column that can be used to store experiment-specific details in String form.

role

The role of the network. By default dallinger initializes all networks as either “practice” or “experiment”

time_of_death

the time at which failing occurred

type

A String giving the name of the class. Defaults to “network”. This allows subclassing.

vars
class psynet.trial.static.StaticNode(*args, **kwargs)[source]

Bases: ChainNode

create_definition_from_seed(seed, experiment, participant)[source]

Creates a node definition from a seed. The seed comes from the previous node in the chain. In many cases (e.g. iterated reproduction) the definition will be trivially equal to the seed, but in some cases we may introduce some kind of stochastic alteration to produce the definition.

Parameters:
  • seed (object) – The seed, passed from the previous state in the chain.

  • experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.

  • participant – The participant who initiated the creation of the node.

Returns:

  • object – The derived definition. Should be suitable for serialisation to JSON.

creation_time

the time at which the Network was created.

details

a generic column for storing structured JSON data

failed

boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason

an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

id

a unique number for every entry. 1, 2, 3 and so on…

network

the network the node is in

network_id

the id of the network that this node is a part of

participant

the participant the node is associated with

participant_id

the id of the participant whose node this is

property1

a generic column that can be used to store experiment-specific details in String form.

property2

a generic column that can be used to store experiment-specific details in String form.

property3

a generic column that can be used to store experiment-specific details in String form.

property4

a generic column that can be used to store experiment-specific details in String form.

property5

a generic column that can be used to store experiment-specific details in String form.

summarize_trials(trials, experiment, participant)[source]

Summarizes the trials at the node to produce a seed that can be passed to the next node in the chain.

Parameters:
  • trials (list) – Trials to be summarized. By default only trials that are completed (i.e. have received a response) and processed (i.e. aren’t waiting for an asynchronous process) are provided here.

  • experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.

  • participant – The participant who initiated the creation of the node.

Returns:

  • object – The derived seed. Should be suitable for serialisation to JSON.

time_of_death

the time at which failing occurred

type

A String giving the name of the class. Defaults to node. This allows subclassing.

vars
class psynet.trial.static.StaticTrial(*args, **kwargs)[source]

Bases: ChainTrial

A Trial class for static experiments.

The user must override the time_estimate class attribute, providing the estimated duration of the trial in seconds. This is used for predicting the participant’s reward and for constructing the progress bar.

time_estimate

The estimated duration of the trial (including any feedback), in seconds. This should generally correspond to the (sum of the) time_estimate parameters in the page(s) generated by show_trial, plus the time_estimate parameter in the page generated by show_feedback (if defined). This is used for predicting the participant’s reward and for constructing the progress bar.

Type:

numeric

participant_id

The ID of the associated participant. The user should not typically change this directly. Stored in property1 in the database.

Type:

int

complete

Whether the trial has been completed (i.e. received a response from the participant). The user should not typically change this directly. Stored in property2 in the database.

Type:

bool

answer

The response returned by the participant. This is serialised to JSON, so it shouldn’t be too big. The user should not typically change this directly. Stored in details in the database.

Type:

Object

earliest_async_process_start_time

Time at which the earliest pending async process was called.

Type:

Optional[datetime]

definition

A dictionary of parameters defining the trial, inherited from the respective Stimulus object.

participant_group

The associated participant group.

block

The block in which the trial is situated.

complete

whether the info is ‘complete’, i.e. has received its contents

creation_time

the time at which the Network was created.

details

a generic column for storing structured JSON data

failed

boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason

an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

id

a unique number for every entry. 1, 2, 3 and so on…

make_definition(experiment, participant)[source]

Creates and returns a definition for the trial, which will be later stored in the definition attribute. This can be an arbitrary object as long as it is serialisable to JSON.

Parameters:
network

the network the info is in

network_id

the id of the network the info is in

origin

the Node that created the info.

origin_id

the id of the Node that created the info

property1

a generic column that can be used to store experiment-specific details in String form.

property2

a generic column that can be used to store experiment-specific details in String form.

property3

a generic column that can be used to store experiment-specific details in String form.

property4

a generic column that can be used to store experiment-specific details in String form.

property5

a generic column that can be used to store experiment-specific details in String form.

show_trial(experiment, participant)[source]

Returns a Page object, or alternatively a list of such objects, that solicits an answer from the participant.

Parameters:
time_of_death

the time at which failing occurred

type

a String giving the name of the class. Defaults to “info”. This allows subclassing.

vars
class psynet.trial.static.StaticTrialMaker(*, id_, trial_class, nodes, expected_trials_per_participant, max_trials_per_participant=None, recruit_mode=None, target_n_participants=None, target_trials_per_node=None, max_trials_per_block=None, allow_repeated_nodes=False, balance_across_nodes=True, check_performance_at_end=False, check_performance_every_trial=False, fail_trials_on_premature_exit=True, fail_trials_on_participant_performance_check=True, n_repeat_trials=0, assets=None, choose_participant_group=None, sync_group_type=None)[source]

Bases: ChainTrialMaker

Administers a sequence of trials in a static experiment. The class is intended for use with the StaticTrial helper class. which should be customised to show the relevant node for the experimental paradigm.

The user may also override the following methods, if desired:

  • choose_block_order(); chooses the order of blocks in the experiment. By default the blocks are ordered randomly.

  • choose_participant_group();

    Only relevant if the trial maker uses nodes with non-default participant groups. In this case the experimenter is expected to supply a function that takes participant as an argument and returns the chosen participant group for that trial maker.

  • on_complete(), run once the sequence of trials is complete.

  • performance_check(); checks the performance of the participant with a view to rejecting poor-performing participants.

  • compute_performance_reward(); computes the final performance reward to assign to the participant.

Further customisable options are available in the constructor’s parameter list, documented below.

Parameters:
  • trial_class – The class object for trials administered by this maker (should subclass StaticTrial).

  • recruit_mode (Optional[str]) – Selects a recruitment criterion for determining whether to recruit another participant. The built-in criteria are "n_participants" and "n_trials".

  • target_n_participants (Optional[int]) – Target number of participants to recruit for the experiment. All participants must successfully finish the experiment to count towards this quota. This target is only relevant if recruit_mode="n_participants".

  • target_trials_per_node (Optional[int]) – Target number of trials to recruit for each node in the experiment. This target is only relevant if recruit_mode="n_trials".

  • max_trials_per_block (Optional[int]) – Determines the maximum number of trials that a participant will be allowed to experience in each block, including failed trials. Note that this number does not include repeat trials.

  • allow_repeated_nodes (bool) – Determines whether the participant can be administered the same node more than once.

  • max_unique_nodes_per_block – Determines the maximum number of unique nodes that a participant will be allowed to experience in each block. Once this quota is reached, the participant will be forced to repeat previously experienced nodes.

  • balance_across_nodes (bool) – If True (default), active balancing across participants is enabled, meaning that node selection favours nodes that have been presented fewest times to any participant in the experiment, excluding failed trials.

  • check_performance_at_end (bool) – If True, the participant’s performance is evaluated at the end of the series of trials. Defaults to False. See performance_check() for implementing performance checks.

  • check_performance_every_trial (bool) – If True, the participant’s performance is evaluated after each trial. Defaults to False. See performance_check() for implementing performance checks.

  • fail_trials_on_premature_exit (bool) – If True, a participant’s trials are marked as failed if they leave the experiment prematurely. Defaults to True.

  • fail_trials_on_participant_performance_check (bool) – If True, a participant’s trials are marked as failed if the participant fails a performance check. Defaults to True.

  • n_repeat_trials (int) – Number of repeat trials to present to the participant. These trials are typically used to estimate the reliability of the participant’s responses. Repeat trials are presented at the end of the trial maker, after all blocks have been completed. Defaults to 0.

  • choose_participant_group (Optional[callable]) – Only relevant if the trial maker uses nodes with non-default participant groups. In this case the experimenter is expected to supply a function that takes participant as an argument and returns the chosen participant group for that trial maker.

  • sync_group_type (Optional[str]) – Optional SyncGroup type to use for synchronizing participant allocation to nodes. When this is set, then the ordinary node allocation logic will only apply to the ‘leader’ of each SyncGroup. The other members of this SyncGroup will follow that leader around, so that in every given trial the SyncGroup works on the same node together.

check_timeout_interval_sec

How often to check for trials that have timed out, in seconds (default = 30). Users are invited to override this.

Type:

float

response_timeout_sec

How long until a trial’s response times out, in seconds (default = 60) (i.e. how long PsyNet will wait for the participant’s response to a trial). This is a lower bound on the actual timeout time, which depends on when the timeout daemon next runs, which in turn depends on check_timeout_interval_sec. Users are invited to override this.

Type:

float

async_timeout_sec

How long until an async process times out, in seconds (default = 300). This is a lower bound on the actual timeout time, which depends on when the timeout daemon next runs, which in turn depends on check_timeout_interval_sec. Users are invited to override this.

Type:

float

network_query

An SQLAlchemy query for retrieving all networks owned by the current trial maker. Can be used for operations such as the following: self.network_query.count().

Type:

sqlalchemy.orm.Query

n_networks

Returns the number of networks owned by the trial maker.

Type:

int

networks

Returns the networks owned by the trial maker.

Type:

list

performance_threshold

Score threshold used by the default performance check method, defaults to 0.0. By default, corresponds to the minimum proportion of non-failed trials that the participant must achieve to pass the performance check.

Type:

float

end_performance_check_waits

If True (default), then the final performance check waits until all trials no longer have any pending asynchronous processes.

Type:

bool